So far, I attended induction programme and arranged my work environment including a new computer and a desk. I had first meeting with my supervisors. Roughly, my PhD will combine two research topics which are multi-agent systems and human-computer interaction. The aim of our research is to develop a well-designed platform in which multiple users and agents interact each other to overcome the peak demand problem of electricity energy market. The topics which are coalition formation algorithms, prediction techniques, game theory, interface designs and usability tests will be examined throughtout the study. The research topic will become more precise after completing required literature reviews.
The papers that I read are as follows:
- Coalitional energy purchasing in the smart grid (Vinyals et al., 2012)
The paper introduces an algorithm to form efficient and stable coalitions which are composed of electricity consumers. The aim of coalition formation is to enable individual consumers to collaborate and benefit from bulk purchasing of electricity. By doing so, suppliers can better estimate load demand and adjust their production effectively, which results in low electricty costs for consumers. The algorithm considers both spot market price and forward market price for coalition value calculation and takes account of social relations among consumers for coalition formation. The emprical evaluation of the coalition formation model demonstrates the following outcomes:
. The density of social network, which is evaluated as the ratio between the number of relations and the number of consumers, affects the stability of coalitions. When the density increases, the stability of coalitions gets more fragile.
. The difference between spot market price and forward market price is important factor to form larger coalitions. The future load demand can be estimated more precisely through the purchases of larger coalitions.
- On coalition formation with sparse synergies (Voice et al., 2012)
The article basically points out the computational redundancy in existing coalition formation algorithms. The redundancy occurs as the existing algorithms assume that all coalitions are feasible to be formed, although there might be constraints (sparse synergies) among coalition members. The article presents novel coalition enumaration and evaluation algorithms where computations are justly shared among agents to increase performance. Moreover, the article introduces an algorithm for coalitional structure generation, which is based on the enumaration and evaluation algorithms. The theoretical and emprical evaluation of the study shows that the algorithms do not include redundant computations and outperform the existing algorithms.
- Competing with humans at fantasy football: team formation in large partially-observable domains (Matthews & Ramchurn, 2012)
I read this article since I am specially interested in playing football and the solution provided by the article might be useful in energy domain as well. The study generates an agent which acts as a football team manager in an online fantasy football game called Fantasy Premier League. The challenge is that the agent (manager) needs to sequentially form an optimal team for each game while there are constraints and uncertainties over players. The manager's decision problem is modeled as partially observable Markov decision process in which the agent needs to maintain the probability distribution over a set of possible states through observations. Here, the possible states correspond to the possible actions that the manager can perform, and the observations are the outcomes of played (or sampled) games and available statistics. The algorithm used in the model is based on model-based Bayesian reinforcement learning which permits more observable quantification of uncertainty and Q-learning that iteratively explores all action space to learn the best qualified actions. The solution is evaluated through created various managers who differ each other in terms of the consideration of the number of future games, the number of generated team samples and the depth of consideration of uncertainty. The outcome of the evaluation shows that Bayesian Q-learning outperforms other attempts in terms of final score.
- Coalitional energy purchasing in the smart grid (Vinyals et al., 2012)
The paper introduces an algorithm to form efficient and stable coalitions which are composed of electricity consumers. The aim of coalition formation is to enable individual consumers to collaborate and benefit from bulk purchasing of electricity. By doing so, suppliers can better estimate load demand and adjust their production effectively, which results in low electricty costs for consumers. The algorithm considers both spot market price and forward market price for coalition value calculation and takes account of social relations among consumers for coalition formation. The emprical evaluation of the coalition formation model demonstrates the following outcomes:
. The density of social network, which is evaluated as the ratio between the number of relations and the number of consumers, affects the stability of coalitions. When the density increases, the stability of coalitions gets more fragile.
. The difference between spot market price and forward market price is important factor to form larger coalitions. The future load demand can be estimated more precisely through the purchases of larger coalitions.
- On coalition formation with sparse synergies (Voice et al., 2012)
The article basically points out the computational redundancy in existing coalition formation algorithms. The redundancy occurs as the existing algorithms assume that all coalitions are feasible to be formed, although there might be constraints (sparse synergies) among coalition members. The article presents novel coalition enumaration and evaluation algorithms where computations are justly shared among agents to increase performance. Moreover, the article introduces an algorithm for coalitional structure generation, which is based on the enumaration and evaluation algorithms. The theoretical and emprical evaluation of the study shows that the algorithms do not include redundant computations and outperform the existing algorithms.
- Competing with humans at fantasy football: team formation in large partially-observable domains (Matthews & Ramchurn, 2012)
I read this article since I am specially interested in playing football and the solution provided by the article might be useful in energy domain as well. The study generates an agent which acts as a football team manager in an online fantasy football game called Fantasy Premier League. The challenge is that the agent (manager) needs to sequentially form an optimal team for each game while there are constraints and uncertainties over players. The manager's decision problem is modeled as partially observable Markov decision process in which the agent needs to maintain the probability distribution over a set of possible states through observations. Here, the possible states correspond to the possible actions that the manager can perform, and the observations are the outcomes of played (or sampled) games and available statistics. The algorithm used in the model is based on model-based Bayesian reinforcement learning which permits more observable quantification of uncertainty and Q-learning that iteratively explores all action space to learn the best qualified actions. The solution is evaluated through created various managers who differ each other in terms of the consideration of the number of future games, the number of generated team samples and the depth of consideration of uncertainty. The outcome of the evaluation shows that Bayesian Q-learning outperforms other attempts in terms of final score.
- Toolkit to support intelligibility in context-aware applications (Lim & Dey, 2010)
In this paper, the necessity of intelligibility of context-aware systems is emphasised. The intelligibility is important for users to build trust to the systems. The paper reviews prevalent decision models like Rules, Decision tree, Bayesian Models and Hidden Markov Models. In addition, explanation types that users need to receive are stated. The paper represents a toolkit which automatically generates explanations for context-aware systems. The toolkit extends the Enactor framework through adding four components which are Explainer, Querier, Reducer and Presenter. In a nutshell, Explainer creates explanation structures, Querier indicates questions and confine explanations, Reducer filters complex explanations to simplify them, and Representer delivers the explanations to users or subsystems. The validation of the toolkit is demonstrated through utilising existing datasets from various context-aware systems in which each system uses different decision model. The study offers a promising approach to investigate how the generated explanations improve user understanding and trust for intelligent applications.
In this paper, the necessity of intelligibility of context-aware systems is emphasised. The intelligibility is important for users to build trust to the systems. The paper reviews prevalent decision models like Rules, Decision tree, Bayesian Models and Hidden Markov Models. In addition, explanation types that users need to receive are stated. The paper represents a toolkit which automatically generates explanations for context-aware systems. The toolkit extends the Enactor framework through adding four components which are Explainer, Querier, Reducer and Presenter. In a nutshell, Explainer creates explanation structures, Querier indicates questions and confine explanations, Reducer filters complex explanations to simplify them, and Representer delivers the explanations to users or subsystems. The validation of the toolkit is demonstrated through utilising existing datasets from various context-aware systems in which each system uses different decision model. The study offers a promising approach to investigate how the generated explanations improve user understanding and trust for intelligent applications.
- Handling of uncertainty in interactive artificial intelligence systems (Hodgson)
This report focuses on the question "Is it possible to improve performance and usability of intelligent systems through interacting with users when there is uncertainty?". To provide an answer to this question, the report proposes two approaches. The first approach delegates decisions to users when there is a high level of uncertainty and agents cannot decide how to react properly. Another approach examined in the report ralates with the visualisation of uncertainty to users. Several display techniques of uncertainty are sampled in the report. The first approach is mostly proved to increase the effectiveness of intelligent systems. However, the effectiveness of the second approach is not certain. It might not be suitable for every domain such as aviation to display uncertainty since the display may complicate the situation.
This report focuses on the question "Is it possible to improve performance and usability of intelligent systems through interacting with users when there is uncertainty?". To provide an answer to this question, the report proposes two approaches. The first approach delegates decisions to users when there is a high level of uncertainty and agents cannot decide how to react properly. Another approach examined in the report ralates with the visualisation of uncertainty to users. Several display techniques of uncertainty are sampled in the report. The first approach is mostly proved to increase the effectiveness of intelligent systems. However, the effectiveness of the second approach is not certain. It might not be suitable for every domain such as aviation to display uncertainty since the display may complicate the situation.
I have also started to read the book "The Design of Everyday Things" by Norman.
What's the common theme you see emerging across these applications? Does the formation of groups and the management of uncertainty related in some form? Where does group interaction generate uncertainty? How would you handle it through good interaction design?
ReplyDeleteThe common theme is to maximise utility through forming teams from individuals who collaborate to achieve same goal. The characteristics (comfort preferences, abilities, etc.) of individuals are not certain, althought they might be predictable. Hence, the outcome of a formed team is uncertain at the beginning. In order to form an ideal team, uncertainties needs to be managed. The uncertainties can be mitigated through well-designed interactions that individuals can express their characteristics.
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